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SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS
Author(s) -
Herbst Edward,
Schorfheide Frank
Publication year - 2014
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2397
Subject(s) - markov chain monte carlo , dynamic stochastic general equilibrium , particle filter , importance sampling , computer science , bayesian probability , bayesian inference , monte carlo method , parallel tempering , metropolis–hastings algorithm , random walk , marginal likelihood , gibbs sampling , posterior probability , inference , hybrid monte carlo , algorithm , sampling (signal processing) , econometrics , mathematics , statistics , artificial intelligence , economics , monetary policy , filter (signal processing) , monetary economics , computer vision , kalman filter
SUMMARY We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random walk Metropolis–Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques; the SMC algorithm is well suited for parallel computing. Copyright © 2014 John Wiley & Sons, Ltd.